size <- 12. Later this will be the number of rows of the matrix.x <- rnorm( size ).x1 by adding (on average 10 times smaller) noise to x: x1 <- x + rnorm( size )/10.x and x1 should be close to 1.0: check this with function cor.x2 and x3 by adding (other) noise to x.size <- 12
x <- rnorm( size )
x1 <- x + rnorm( size )/10
cor( x, x1 )
[1] 0.9974836
x2 <- x + rnorm( size )/10
x3 <- x + rnorm( size )/10
x1, x2 and x3 column-wise into a matrix using m <- cbind( x1, x2, x3 ).m.m.heatmap( m, Colv = NA, Rowv = NA, scale = "none" ).m <- cbind( x1, x2, x3 )
class( m )
[1] "matrix"
head( m )
x1 x2 x3
[1,] -1.8008075 -1.59680573 -1.79657105
[2,] -0.1171613 -0.32607067 -0.18802299
[3,] -1.4861007 -1.58203474 -1.44892725
[4,] -0.1141060 -0.01751122 -0.05080385
[5,] 2.7553152 2.97429438 2.72284045
[6,] 0.2283969 0.31363577 0.28877318
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
# x1, x2, x3 follow similar color pattern, they should be correlated
y1…y4 (but not correlated with x), of the same length size.m from columns x1…x3,y1…y4 in some random order.y <- rnorm( size )
y1 <- y + rnorm( size )/10
y2 <- y + rnorm( size )/10
y3 <- y + rnorm( size )/10
y4 <- y + rnorm( size )/10
m <- cbind( y4, y3, x2, y1, x1, x3, y2 )
heatmap( m, Colv = NA, Rowv = NA, scale = "none" ) # high is dark red, low is yellow
cc <- cor( m ) to build the matrix of correlation coefficients between columns of m.round( cc, 3 ) to show this matrix with 3 digits precision.cc <- cor( m )
round( cc, 3 ) #
y4 y3 x2 y1 x1 x3 y2
y4 1.000 0.994 -0.270 0.993 -0.314 -0.283 0.995
y3 0.994 1.000 -0.299 0.994 -0.341 -0.315 0.996
x2 -0.270 -0.299 1.000 -0.284 0.995 0.994 -0.269
y1 0.993 0.994 -0.284 1.000 -0.327 -0.298 0.994
x1 -0.314 -0.341 0.995 -0.327 1.000 0.997 -0.308
x3 -0.283 -0.315 0.994 -0.298 0.997 1.000 -0.280
y2 0.995 0.996 -0.269 0.994 -0.308 -0.280 1.000
heatmap( cc, symm = TRUE, scale = "none" )
# E.g. value for (row: x1, col: y1) is the corerlation of vectors x1, y1.
# Values of 1.0 are on the diagonal: e.g. x1 is perfectly correlated with x1.
# Correlations between x, x vectors are close to 1.0.
# Correlations between y, y vectors are close to 1.0.
# Correlations between x, y vectors are close to 0.0.
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